In many clinical and epidemiological studies one of the objectives is to evaluate changes in the response variable over time. In such studies subjects are followed, and their responses are measured at several time points during the study period. It is common in these studies to lose subjects due to death, withdrawal and loss to follow-up. Further attrition may be related to the response, causing the data to be informatively censored. For example, withdrawals may occur at greater frequency among those who do not respond to the treatment than those who do. Informative right censoring poses a challenge to statisticians since many of the popularly used statistical methods assume that the attrition probability is independent of the response variable. The proposed project will focus on the analysis of longitudinal data when the censoring process is considered informative and will develop statistical methods for both continuous and discrete longitudinal data. Application to cancer studies will be emphasized, and the developed methods will be illustrated using actual data from clinical and epidemiological studies of cancer.